class_docstr: Split-Attention Conv2d in ResNeSt.
  
  Args:
      in_channels (int): Number of channels in the input feature map.
      channels (int): Number of intermediate channels.
      kernel_size (int | tuple[int]): Size of the convolution kernel.
      stride (int | tuple[int]): Stride of the convolution.
      padding (int | tuple[int]): Zero-padding added to both sides of
      dilation (int | tuple[int]): Spacing between kernel elements.
      groups (int): Number of blocked connections from input channels to
          output channels.
      groups (int): Same as nn.Conv2d.
      radix (int): Radix of SpltAtConv2d. Default: 2
      reduction_factor (int): Reduction factor of inter_channels. Default: 4.
      conv_cfg (dict): Config dict for convolution layer. Default: None,
          which means using conv2d.
      norm_cfg (dict): Config dict for normalization layer. Default: None.
      dcn (dict): Config dict for DCN. Default: None.
      init_cfg (dict or list[dict], optional): Initialization config dict.
          Default: None
docstr: nn.Module: the normalization layer named "norm0" 
docstr_tok: ["nn.Module",":","the","normalization","layer","named","``","norm0","''"]
code: def norm0(self):
      """nn.Module: the normalization layer named "norm0" """
      return getattr(self, self.norm0_name)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from mmengine.model import BaseModule
  from mmdet.registry import MODELS
  from ..layers import ResLayer
  from .resnet import Bottleneck
  from .resnet import ResNetV1d
label_desc: Use image node_id 2 for calling the SplitAttentionConv2d obj's underlying member method code with example usage: obj.norm0() and returns: getattr